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03/07/2023
Extremes in High Dimensions: Methods and Scalable Algorithms
Extreme-value theory has been explored in considerable detail for univar...
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03/03/2023
Lag selection and estimation of stable parameters for multiple autoregressive processes through convex programming
Motivated by a variety of applications, high-dimensional time series hav...
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02/22/2023
The DeepCAR Method: Forecasting Time-Series Data That Have Change Points
Many methods for time-series forecasting are known in classical statisti...
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02/16/2023
Reducing Computational and Statistical Complexity in Machine Learning Through Cardinality Sparsity
High-dimensional data has become ubiquitous across the sciences but caus...
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12/11/2022
Statistical guarantees for sparse deep learning
Neural networks are becoming increasingly popular in applications, but o...
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06/21/2022
Marginal Tail-Adaptive Normalizing Flows
Learning the tail behavior of a distribution is a notoriously difficult ...
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05/09/2022
Statistical Guarantees for Approximate Stationary Points of Simple Neural Networks
Since statistical guarantees for neural networks are usually restricted ...
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02/02/2022
VC-PCR: A Prediction Method based on Supervised Variable Selection and Clustering
Sparse linear prediction methods suffer from decreased prediction accura...
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01/13/2022
Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method
Deep sequencing has become one of the most popular tools for transcripto...
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07/15/2021
Copula-Based Normalizing Flows
Normalizing flows, which learn a distribution by transforming the data t...
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06/04/2021
Regularization and Reparameterization Avoid Vanishing Gradients in Sigmoid-Type Networks
Deep learning requires several design choices, such as the nodes' activa...
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05/28/2021
Targeted Deep Learning: Framework, Methods, and Applications
Deep learning systems are typically designed to perform for a wide range...
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05/28/2021
DeepMoM: Robust Deep Learning With Median-of-Means
Data used in deep learning is notoriously problematic. For example, data...
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01/25/2021
Activation Functions in Artificial Neural Networks: A Systematic Overview
Activation functions shape the outputs of artificial neurons and, theref...
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10/02/2020
No Spurious Local Minima: on the Optimization Landscapes of Wide and Deep Neural Networks
Empirical studies suggest that wide neural networks are comparably easy ...
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09/14/2020
Risk Bounds for Robust Deep Learning
It has been observed that certain loss functions can render deep-learnin...
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09/13/2020
Is there a role for statistics in artificial intelligence?
The research on and application of artificial intelligence (AI) has trig...
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06/28/2020
Layer Sparsity in Neural Networks
Sparsity has become popular in machine learning, because it can save com...
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05/30/2020
Statistical Guarantees for Regularized Neural Networks
Neural networks have become standard tools in the analysis of data, but ...
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05/01/2020
Thresholded Adaptive Validation: Tuning the Graphical Lasso for Graph Recovery
The graphical lasso is the most popular estimator in Gaussian graphical ...
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04/24/2020
Estimating the Lasso's Effective Noise
Much of the theory for the lasso in the linear model Y = X β^* + ε hinge...
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02/27/2020
Tuning-free ridge estimators for high-dimensional generalized linear models
Ridge estimators regularize the squared Euclidean lengths of parameters....
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09/23/2019
Tuning parameter calibration for prediction in personalized medicine
Personalized medicine has become an important part of medicine, for inst...
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07/08/2019
False Discovery Rates in Biological Networks
The increasing availability of data has generated unprecedented prospect...
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07/08/2019
Aggregated False Discovery Rate Control
We propose an aggregation scheme for methods that control the false disc...
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01/04/2018
Prediction Error Bounds for Linear Regression With the TREX
The TREX is a recently introduced approach to sparse linear regression. ...
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10/09/2017
Maximum Regularized Likelihood Estimators: A General Prediction Theory and Applications
Maximum regularized likelihood estimators (MRLEs) are arguably the most ...
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04/10/2017
Integrating Additional Knowledge Into Estimation of Graphical Models
In applications of graphical models, we typically have more information ...
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10/01/2016
Tuning Parameter Calibration in High-dimensional Logistic Regression With Theoretical Guarantees
Feature selection is a standard approach to understanding and modeling h...
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09/23/2016
Efficient Feature Selection With Large and High-dimensional Data
Driven by the advances in technology, large and high-dimensional data ha...
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08/01/2016
Oracle Inequalities for High-dimensional Prediction
The abundance of high-dimensional data in the modern sciences has genera...
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04/22/2016
Non-convex Global Minimization and False Discovery Rate Control for the TREX
The TREX is a recently introduced method for performing sparse high-dime...
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10/27/2014
Topology Adaptive Graph Estimation in High Dimensions
We introduce Graphical TREX (GTREX), a novel method for graph estimation...
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09/16/2014
Compute Less to Get More: Using ORC to Improve Sparse Filtering
Sparse Filtering is a popular feature learning algorithm for image class...
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04/02/2014
Don't Fall for Tuning Parameters: Tuning-Free Variable Selection in High Dimensions With the TREX
Lasso is a seminal contribution to high-dimensional statistics, but it h...
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02/07/2014